Abstract: A method of searching for one of a plurality of objects that matches an input sequence of handwritten objects is provided. The objects are modeled by concatenating members of a set of component objects. A Trie structure representing the plurality of objects is generated. Component objects of each object are assigned to the elements of respective nodes of the Trie structure. A respective hidden Markov model (HMM) is associated with each element of each non-leaf node. The HMMs represent the respective component object of the element. A maximum probability of any HMM accepting any of the set of component objects is estimated. The root node of the Trie structure is selected. A plurality of elements of the selected node are selected. A plurality of segments of the input sequence are applied to respective HMMs associated with the selected elements to generate respective acceptance values.

Abstract: A method for indexing a plurality of handwritten objects is provided. A B-tree data structure of order m is generated, where m is an integer. The B-tree has a plurality of nodes divided into a plurality of levels ordinally numbered 0.sup.th through n.sup.th. Each node in the 0.sup.th level is a leaf. Each node in the 1.sup.th level has at least m/2 leaves as children. Each one of the handwritten objects is assigned to a respective leaf. A respectively different hidden Markov model (HMM) is associated with each respective child of each of the nodes in the 1.sup.th to n.sup.th levels. Each one of the nodes in the 1.sup.th to n.sup.th levels contains the respective HMM associated with the child of the one node. Each HMM in each one of the nodes in the 1.sup.th level is trained to accept the handwritten object of the respective leaf that is a child of the one node. Each HMM associated with any of the nodes in the 2.sup.th through n.sup.

Abstract: A method for indexing electronic handwritten documents is provided. Each document includes a plurality of output symbols in an output sequence, and is modeled by a respective Hidden Markov Model (HMM). The HMMs share a common alphabet and a common sequence length. A tree is established, having linked nodes stored in a memory. Each node has n pointers, each identifying a different node in the next level of the tree. Each path from the root to a different one of the leaf nodes defines a respective sequence of pointers. An indexing procedure is performed, for each of a subset of the nodes in one of the levels of the tree.

Abstract: A method of searching a database having a plurality of objects is provided. Each object includes attributes and, for each attribute, a number of values. A query specifies two attributes and a maximum distance. A respective set of ranges is established for each object that has a value for the first attribute. Each set includes a range for each value of the first attribute. Each range is defined by minimum and maximum location values. A test range is established for one of the ranges. The test range has values equal to the minimum and maximum values of one of the ranges. The test range is adjusted, if necessary, so that it includes one of the values of the second attribute of the corresponding object. The test range is added to a group of ranges corresponding to the object if the minimum and maximum test values do not differ from one another by more than the maximum distance.